Make your apps and your business smarter with machine learning

Are you still wondering what Data Science and Machine Learning can do for you? Feels out of your reach? Don't know how to get started?

In the age of Big Data, Machine Learning and Data Science seem to be all the rage. Prediction APIs are now making them accessible to everyone and this book is the first that teaches how to use them. You will learn the possibilities they offer, how to formulate your own Machine Learning problem, and how to address it with the APIs — not with complex algorithms.

Bootstrapping Machine Learning is the first book of its kind and it’s the ideal resource to get started with prediction services. Our clients have been asking for something like this for some time. We are now ordering copies in bulk for them!

Data alone is not enough. We need predictive applications to make it valuable, actionable and meaningful. This is an ideal book for business professionals who want to understand what the heck is machine learning and what it can do for their business.

Discover opportunities

Be a step ahead of the competition and figure out how to exploit the value of data in your business or in your app. A whole chapter of the book is dedicated to concrete examples so you’ll understand why learning from data is so important and what are the opportunities. The book also teaches you what makes ML work and what are the limitations, so you’ll be able to develop your own original ideas of ML applications.

Soon you'll be able to...

Personalize and improve users' experience on your app, by observing their behavior, then predicting their needs and interests.

Take control

You don't need to hire an expert to make Machine Learning work for you. In fact, the most important things are to incorporate your domain knowledge into the ML system and to figure out how predictions can create value: with a little training, you'll be the best person for the job. Also, you'll save a lot of money by handling things yourself — Data Scientists aren't cheap.

Don’t learn algorithms — learn Predictive APIs

Today's technology abstracts away the complexities of creating predictive models with Machine Learning. You don't have to worry about algorithmics any more and you can focus on the most critical aspects for the success of your Machine Learning application: preparing data and acting on predictions. While others are investing a lot of time and money building their own solutions based on traditional ML algorithms, you can be much quicker by adopting Predictive APIs. Bootstrapping Machine Learning is the first book that teaches you how. You could be creating your first ML system within a few hours, literally!

I recommend this book to a developer or startup looking to start using machine learning quickly and effectively.

The book is clearly presented with the content focused and well suited for the audience. It is not maths heavy, nor is it bogged down with pages and pages of code examples. I really like the crisp presentation of two APIs focused in the book – Google Prediction API and BigML and the world example is just the right level of detail. 

The most in-demand skills today

Building predictive apps and predictive businesses is a very hot topic. But don’t just take my word for it. Here is what the experts are saying:

For apps

"It’s difficult to imagine a new tech company launching that doesn’t at least consider using machine learning models to make its product or service more intelligent." — Derrick Harris, Senior writer at Gigaom

"There’s no doubt that developers are going to be increasingly asked to embed [predictive] analytics capabilities within their applications.” — Michael Vizard, IT Business Edge

For businesses

“In today’s mobile-first digital world, it’s not enough to understand what your customers have done in the past. The most successful digital businesses will predict customer needs and take action to address them.” — Chet Kapoor, Apigee CEO

“Using historical measures to gauge business and process performance is a thing of the past. To prevail in challenging market conditions, businesses need predictive metrics." — Samantha Searle, Research Analyst at Gartner

In general

"In the next 20 years, machine learning will have more impact than mobile has." — Vinod Khosla, Founder of Khosla Ventures

About the author

Hi! My name is Louis Dorard, I am a consultant in predictive technology with a PhD in Machine Learning. I am also a co-founder and the General Chair of PAPIs.io, the International Conference on Predictive APIs and Apps.

I love starting things from scratch, I am a big believer in simplicity, and I am passionate about the web. Prior to writing this book, I served as Chief Science Officer in a startup and I founded a web-based business.

My goal is to help you create smarter apps and businesses by using Machine Learning and Prediction APIs. Read more about me...

Louis presents machine learning in a way that is both extremely approachable and directly applicable — a winning combo.

— Guillaume Bazouin, Project Coordinator - Analyst at Stanford

The book’s case study is really awesome! You follow Louis’s reasoning on a concrete example that you can relate to. All the steps and choices made along the way are explained in a very clear way. The results are extremely interesting. The analysis and recommendations make perfect sense. Great job :-)

More than just a book

If I’ve had to waste time figuring out the quirks and gotchas of Prediction APIs, you don’t have to. So in addition to the book itself, I have created additional resources that will save you lots of time when getting started with BigML and Google Prediction API:

Screencasts that show you how to set up and use these services. They are video recordings of my screen with live audio comments of everything you need to do to start making predictions and they show you all the steps to reproduce.

Interactive code tutorials, in the form of IPython notebooks in the browser. Essentially, they are web pages in which there are blocks of code that you can edit and run. IPython notebooks are a great way to see how to use the APIs step by step and to learn things about them in between blocks of code.

Code, datasets and a Virtual Machine powered by Vagrant, for you to get hacking with the APIs in no time. I'm giving you my code to evaluate their performance on your own dataset, as described in the book. The code is in Python, which is the language of choice for Data Science and hacking.

The Complete Digital Package ($299)

For professionals who are serious about using Prediction APIs and want to get the most value for money. It's everything you need to make Machine Learning work for you. These resources are going to save you a few days' work and I am only charging $299 for the complete digital package. If your time is valuable, you'll be much better off getting this than figuring out everything on your own.

★ Resources

The ebook in PDF, ePub and Mobi formats. 196 pages to teach you everything you need to know about Machine Learning so you can use Prediction APIs effectively.

★ Tutorials

Screencasts

Screencasts give you something to refer to when setting up your own user accounts and projects. Here's what you get:

An introduction to Google Apps Scripts

Setting up and getting started with Google Prediction API through the web-based interface, Google Cloud Console

How to fill in missing values with predictions in a Google Spreadsheet

An overview of the BigML.com web-based interface.

Prediction Google Spreadsheet

Google Spreadsheets are the equivalent of Excel files, in the cloud and in the browser. Similarly to VBA macros, Google Apps Scripts allow to create functions that manipulate the contents of a Google Spreadsheet and that read/write values in its cells. When coupled with Google Prediction API, this gives a spreadsheet that can fill in its missing values with predictions. You'll get access to my Prediction Google Spreadsheet, its associated Google Apps Scripts code, and updates.

IPython notebooks

When I want to bring someone up to speed on Prediction APIs, I have them go through my notebooks. Now, you can use them too to:

Get started with Google Prediction API

Get started with the BigML API

Make batch predictions with Google Prediction API on a given test dataset

Make batch predictions with BigML.

Bonus chapter: "What next?"

What can you do once you've made these Prediction APIs work for you? Read about how to valorize your work through Intellectual Property and how to have experts improve on your work with Data Science crowdsourcing.

★ 3 Bonus videos

I have made 3 videos to complement the learning experience.

The 1st one recaps the key take-aways of the book.

The 2nd one is a discussion on how to beat the competition at Machine Learning.

The 3rd one is a short tutorial to using "ensembles" to boost predictions' accuracy.

★ Premium membership

Get access to more exclusive content, delivered directly to your inbox after your purchase. Continue to discover new things about Machine Learning and Prediction APIs!

★ Code and Virtual Machine

Performance evaluation code in Python

Evaluate the performance of your system in advance of its deployment. Compare Google Prediction API and BigML on your own data and see what works best for you. It’s as simple as running

You can't do Machine Learning without data. Start exploring Prediction APIs with various sample datasets in CSV format. You can use them when learning and testing the APIs, or you can just browse them to see what the data used in Machine Learning looks like.

Virtual Machine

The best thing to do to avoid any issues specific to your machine and to make sure that everything's in place for you to start hacking with Prediction APIs is to use a Virtual Machine that bundles code, sample datasets, and that has everything installed (Python, API wrappers, etc.).

The VM I'm providing is based on Ubuntu. It runs with Virtualbox (a free virtualization software) and you can recreate it in two command lines with Vagrant (a tool that allows to script the creation of VMs).

Case study data extraction

Get access to the Google Apps Script project I made to collect my email data in the Priority Inbox case study of the book. The code (in GS which is a sublanguage of Javascript) will show you how to extract your own data from Google services, how to extract features, and how to create a dataset to do Machine Learning with.

★ 3 months of BigML Boosted

Use BigML for free for 3 months with the Boosted plan, which gives you an un limited number of tasks of up to 1 GB in size each, with up to 4 tasks in parallel. You can activate this offer within 12 months, whenever you're ready. The BigML Boosted plan is worth $360 when buying separately and will suit almost any professional usage.

(Note: I am not affiliated with BigML and I do not have a financial incentive to put their service forward.)

The Premium Digital Package ($149)

If you can't afford the complete digital package, but still want to save time to set up Prediction APIs, I am offering a cheaper option at $149 with the ebook plus extra resources plus tutorials (screencasts and IPython notebooks) to show you exactly how to get started with BigML and Google Prediction API. Again, if you do the math, the time savings are worth it!

★ Premium membership

Get access to more exclusive content, delivered directly to your inbox after your purchase. Continue to discover new things about Machine Learning and Prediction APIs!

★ 2 months of BigML Standard

Use BigML for free for 2 months with the Standard plan, which gives you an un limited number of tasks of up to 64 MB in size each, with up to 2 tasks in parallel. You can activate this offer within 12 months, whenever you're ready. The BigML Standard plan is worth $60 when buying separately and it is suitable for small applications in a professional context.

(Note: I am not affiliated with BigML and I do not have a financial incentive to put their service forward.)

The Ebook Only ($39)

The budget option at just $39, including the ebook in PDF, ePub and Mobi formats.

Even though the additional resources found in the packages are extremely useful, the central piece is the ebook itself and you'll gain valuable knowledge from it. It's 196 pages to teach you how to make Machine Learning work for you with Prediction APIs.

FAQ

Can I get the book in print?

Yes! For now it's only available on Amazon — which means that it's not included in the packages. If you want both editions, print and digital, you should first buy the book on Amazon and then forward your receipt email to bml-ebook@codole.com. You will then receive instructions to get the ebook in PDF/ePub/Mobi and you will also be able to upgrade to one of the packages.

Is this book for me?

This book is for computer engineers, scientists, hackers, programmers, CTOs, analysts, thinkers...If you've ever used Excel and written as little as 3 lines of code in your life, then you have all the prerequisites to start learning about Machine Learning, what it can do for you, and how to put it to practise. You can also have a look at the book sample and see if you like it or not.

What if I don't like it?

I believe that if you hate the book/package, then I shouldn't keep your money. Just reply to your purchase receipt email within 30 days to explain why you hated it and I will issue a full refund (note that I am unable to issue refunds if you purchased on Amazon).

Which package should I get?

If you're a professional or a developer who's serious about using Prediction APIs, you should most definitely get the complete digital package. Otherwise, if you don't care too much for code but still want to make the most of these APIs through a web interface, you should get the premium digital package. If budget is an issue, you can get the ebook only, you won't be disappointed.

Do you offer discounts?

Only to students, public researchers and people working in NGOs: they get 50% off on the digital editions bought from this site (not on Amazon). If you qualify, please send supporting documentation to discount@codole.com to get a coupon.

I have another question...

If you still have questions after reading this page, please get in touch and I'll do my best to answer them!

This book is very good. I love the formalization of ML problems that you provide. I wish I’d read this earlier in my life!